The use of chronosequences in studies of ecological succession ...

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730 L. R. Walker et al.CHRONOSEQUENCES AS NULL MODELS ANDPREDICTIVE TOOLSTriggerDisturbanceCritical observationsDurationShort-, mid-, long-termCharacteristicsProgressive, retrogressiveTrajectory patternCyclical, linear, convergent,divergent, networkInterpretation of successionInterpolationExtrapolationInfluencesAbiotic or bioticStochastic or deterministicInternal or externalManagementoutcomesImproved:PredictabilityManipulationFig. 2. Elements for a chronosequence function of a general successionalmodel. Following a disturbance, changes in vegetation or soiloccur and the chronosequence approach can be used to determine theduration, characteristics and trajectory pattern. In addition, criticalabiotic and biotic influences can be determined and characterized.The more extensive description and quantification that can beobtained about an ecosystem, the better the interpretation can be ofsuccessional patterns via interpolation within and extrapolationbeyond the available data sets. Ultimately, chronosequence tools canaid management by improving the prediction of successional changeand its manipulation through such efforts as conservation or restoration.The assumption that a chronosequence exists across varioussites with certain patterns of changing traits provides a usefulnull model that can be verified or refuted with further observationand experimentation. With this approach, useful lessonscan be learned even when erroneous assumptions about thechronosequence have been made. For example, studies onsand dunes (Olson 1958; Boerner 1985) that initially assumed alinear successional trajectory have led to the discovery of nonlinearsuccessional networks. Similarly, assumptions about theprogressive nature of successional properties have been modifiedby the recognition of the retrogressive phase of long-termchronosequences (Walker & Reddell 2007). The developmentof predictive models of successional trajectories is difficultbecause of our poor understanding about how complex processessuch as dispersal, colonization and competition unfoldin space and time (Pickett, Cadenasso & Meiners 2009). Lessonslearned from chronosequence studies about convergence,deterministic consequences of certain dominant life-forms, orpatterns of retrogression can become inputs into a chronosequencefunction of a general model of succession (Fig. 2;Walker & del Moral 2003). Clarifying such variables can helpinterpret successional pathways through either interpolationbetween data on stages of known ages or extrapolation beyondknown data to future pathways (completing the dotted lines –particularly for the trajectories shown on the left side ofFig. 1). For example, if short-term chronosequence observations(years to decades) on landslides suggest initial convergencewithin a progressive succession caused by bioticcolonization processes (Guariguata 1990; Walker et al. 1996)and soil development (Zarin & Johnson 1995), extrapolationto longer time periods will be robust and interpolations can bemade about intermediate stages. If restoration of a landslide isdesired, manipulations improve when trajectories are understood(Walker, Vela´zquez & Shiels 2009). Chronosequencesbecome essential predictive tools when considering trajectoriesof community and ecosystem processes at long-term (millennial)scales (Walker et al. 2000). Any such model mustaccount, of course, for the often nonlinear nature of vegetationchange by allowing for both deterministic and stochasticaspects of temporal dynamics (Cramer 2007).Where chronosequences are least appropriateThe assumption of many ecologists in the early 20th centurywas that the present repeats the past (McIntosh 1985), so chronosequenceswere widely used to interpret temporal patterns.The subsequent shift to a more reductionistic perspective anddecades of experimental manipulations indicate that successionis often not deterministic (Glenn-Lewin, Peet & Veblen1992). Therefore, we assert that chronosequences should notbe used to infer short- and mid-term successional dynamicswhen the sites are not temporally related in a linear fashion orwhen they have different vegetation histories due to climatic,landscape or stochastic factors (Walker & del Moral 2003).One such example involves toposequences, where differencesin plant communities are influenced by their position on thelandscape (Matthews & Whittaker 1987; Avis & Lubke 1996)more than by temporal dynamics. Other conditions wherechronosequences are least appropriate include divergent trajectories,highly disturbed seres, or seres with slow rates of turnover,which we now discuss in turn.DIVERGENT AND NONLINEAR SERESWhen successional trajectories are divergent or are configuredas nonlinear networks, the chronosequence approach is lessuseful and may require more intensive sampling than for parallelor convergent seres (Fig. 1). Divergence is common due topriority effects (i.e. sequence of species arrivals), sensitivity tominor differences in initial conditions, stochastic effects andinitial site heterogeneity (Matthews & Whittaker 1987). Earlysuccessional communities may more closely resemble eachother, particularly in severely disturbed habitats with few successfulcolonists, while later successional stages with higherbiodiversity diverge. High regional biodiversity can contributeto high within-stand diversity and therefore also increase thelikelihood of divergence. Local convergence may occur wherecertain successful species dominate, but divergence may existat larger spatial scales (Lepsˇ &Rejmánek 1991). Networksoccur when there are multiple stages that arise from a singlestage, resulting in alternative pathways to a convergentendpoint or continued divergence. Causes of networks includedifferent initial site conditions or stochastic dispersal thatÓ 2010 The Authors. Journal compilation Ó 2010 British Ecological Society, Journal of Ecology, 98, 725–736
Chronosequences, succession and soil development 731results in different pioneer communities, leading to independentand sometimes parallel trajectories (Walker & delMoral 2003). Each additional layer of complexity challengesassumptions of connectivity where interpolation is usedbecause of missing data sets and makes the application of thechronosequence approach more difficult.DISTURBED SERESWhen severe or frequent disturbances reset a sere, successionmay be deflected, thus reducing the value of the chronosequenceapproach. Deflections occur in a variety of ways due tothe differential responses of organisms over time and the natureof the repeat disturbances such as moving dunes (Castillo,Popma & Moreno-Casasola 1991) or repeated floods (Baker &Walford 1995). Alternatively, subsequentdisturbancesmaynot reset a general successional trend, even if they are relativelysevere, as found for early succession on Puerto Rican landslides(Walker & Shiels 2008) or in fire-driven ecosystems innorthern Sweden (Wardle et al. 1997). Deflected seres aretypically caused by allogenic disturbances (e.g. flood, invasivespecies) but can be reinforced through autogenic processes(e.g. grazing), especially those leading to retrogression (Walker& del Moral 2009). When the timing or severity of thedisturbance is unknown (e.g. historic dune migrations), there isno historic baseline and chronosequences are hard toapply. Conversely, with well-documented disturbances (e.g.abandonment of agricultural fields; Cramer & Hobbs 2007) orartificial events (e.g. experimental blowdowns of trees;Cooper-Ellis et al. 1999), details about the timing and severityof the disturbance can help to clarify subsequent trajectoriesand improve the application of the chronosequenceapproach.SLOW OR ARRESTED SERESRates of plant succession vary from rapid change to almost nochange at all. Chronosequences are most applicable to the former;however, changes in ecosystem processes can occur evenwhen all stages are dominated by the same plant species, suchas in monospecific New Zealand mountain beech (Nothofagussolandri) stands (Clinton, Allen & Davis 2002). Succession canbe arrested due to abiotic constraints (e.g. nutrient limitation),limitations in the size of the regional species pool, or resourceusedomination by a species leading to competitive inhibitionof other species, at least until the dominant species senesces(Walker & del Moral 2003). Both native and invasive speciescan dominate a successional stage, typically by monopolizinglight, water and nutrients through the formation of mats orthickets composed of algae (Benedetti-Cecchi & Cinelli 1996),mosses (Cutler, Belyea & Dugmore 2008), cryptogamic crusts(Kaltenecker, Wicklow-Howard & Pellant 1999), grasses (Nakamura,Yajima & Kikuchi 1997), vines (Melick & Ashton1991), ferns (Russell, Raich & Vitousek 1998), shrubs (Young,Shao & Porter 1995) or trees (Dickson & Crocker 1953). Earlyrecognition of arrested states will allow examination of thecause and potentially lead to the discovery of other controllingvariables, but the chronosequence approach is not easilyapplied to such situations.How to improve the use of chronosequencesCategorical generalizations about when it is appropriate orinappropriate to use chronosequences to study succession orsoil development are not possible, because successional trajectoriescan be complex and difficult to predict (Walker & delMoral 2003). However, the relative merits of applying chronosequencescan be compared for different trajectories and communitycharacteristics (Table 2). We suggest thatchronosequences work better with predictable than unpredictableseres, but unpredictable, convergent seres can often beanalysed with some reliability. These relationships apply toeither progressive or retrogressive seres. In contrast, we proposethat local community biodiversity and disturbance effectson the usefulness of chronosequences differ between progressiveand retrogressive seres for studies of plant successionunder conditions of high disturbance. High plant species diversityin the regional species pool can make chronosequenceapproaches difficult because of the greater potential for colonizationof different sites at the same stage by different speciesleading to alternative trajectories (Matthews 1992; Prach1994), especially in highly disturbed habitats (MacDougall,Wilson & Bakker 2008). Soil development is less affected thanplant succession by plant species diversity, but it is still lesslikely to be amendable to study by chronosequence approacheswhen diversity is high and when there is high disturbance. Inretrogressive seres, chronosequences can also sometimes be difficultto apply (especially for plant succession), even at low levelsof biodiversity, due to the larger potential for divergence(Table2).Again,soildevelopmentissomewhatbufferedfromthese problems.The process of soil development encompasses a time span ofcenturies to millennia and is arguably more deterministic thansuccession once the roles of climate and parent material areclarified (Jenny 1980). Chronosequences are thus interpretedas a series of soils of different ages that formed on the sameparent material, and can be highly appropriate for addressingquestions about soil development and its effects on communityand ecosystem properties. Such uses of chronosequences havesignificantly advanced our understanding of how soil nutrientschange during pedogenesis (Walker & Syers 1976; Vitousek2004) and the impact of changes in soil nutrient availability onplants (Wardle et al. 2008), decomposers (Williamson, Wardle& Yeates 2005; Doblas-Miranda et al. 2008), foliar herbivores(Gruner 2007) and above-ground and below-ground ecosystemprocesses (Crews et al. 1995; Wardle, Walker & Bardgett2004; Whitehead et al. 2005). Chronosequences can be used inthis way to clarify the effects of soil age on current plant communityattributes (Wardle et al. 2008), even when they do notgenerate insights about patterns of plant succession.When observations of long-term chronosequences are combinedwith experiments (Fukami & Wardle 2005), furtherinsights are gained about the mechanistic basis of communityand ecosystem change. For example, controlled fertilizerÓ 2010 The Authors. Journal compilation Ó 2010 British Ecological Society, Journal of Ecology, 98, 725–736
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